Dynamic object detection using lidar data for autonomous machine systems and applications

ABSTRACT

In various examples, systems and methods of the present disclosure detect and/or track objects in an environment using projection images generated from LiDAR. For example, a machine learning model—such as a deep neural network (DNN)—may be used to compute a motion mask indicative of motion corresponding to points representing objects in an environment. Various input channels may be provided as input to the machine learning model to compute a motion mask. One or more comparison images may be generated based on comparing depth values projected from a current range image to a coordinate space of a previous range image to depth values of the previous range image. The machine learning model may use the one or more projection images, the one or more comparison images, and/or the one or more range images to compute a motion mask and/or a motion vector output representation.

BACKGROUND

The ability to safely detect static and dynamic features and objects inan environment is an important task for any autonomous orsemi-autonomous system—such as an autonomous or semi-autonomous drivingsystem. For example, by observing movement—or the lack thereof—acrossframes, static features or dynamic actors may be identified to aid invarious downstream tasks, such as object tracking, path planning,obstacle avoidance, control decisions, and/or the like.

Some conventional approaches use an object detector that may execute onone or more frames of data to match objects or features across frames,and then to infer motion from the matched features or objects. However,such conventional approaches are limited by the object detectors abilityto identify objects, which requires prior knowledge of each object orfeature type to be detected. For example, these systems often requireextensive training using large amounts of training data includingdepictions of the particular objects the detector is trained to detect.However, given that there may be nearly unlimited types of objects,these conventional approaches are often inadequate and/or incompletesolutions for detecting and tracking objects.

Other systems may employ optical flow approaches to find a pixel-levelflow field from one frame to a subsequent frame based on analyzing atime series of frames. These conventional approaches require that eachframe—e.g., each LiDAR range image—includes adequate texture informationto allow for accurate tracking across frames. However, generatingdata—especially LiDAR data—with adequate texture information ischallenging due to LiDAR sensor viewpoint changes and/or potential sceneocclusion. While some conventional systems may combine the aboveapproaches, these combinations do not overcome many of the shortcomingsof these conventional solutions.

SUMMARY

Embodiments of the present disclosure relate to detecting static anddynamic features from LiDAR in autonomous machine applications. Systemsand methods are disclosed that determine motion based on one or morerange images and one or more projection images.

In contrast to conventional systems, such as those described above,systems and methods of the present disclosure detect and/or trackobjects (e.g., static and/or moving objects) in an environment usingprojection images—e.g., LiDAR range images, top-down or birds eye viewprojection images, etc.—of points clouds and/or other detectionrepresentations. For example, a machine learning model—such as a deepneural network (DNN)—may be used to compute a motion mask or otheroutput type indicative of motion corresponding to points or pixelsrepresenting objects or features in an environment. Various inputchannels may be provided as input to the machine learning model to aidthe machine learning model in computing the output. For example, one ormore projection images may be generated based on projecting depth valuesfrom a current range image to a coordinate space of a previous rangeimage and/or projecting depth values from a previous range image to acoordinate space of a current range image. In some embodiments, one ormore comparison images may be generated based on comparing depth valuesprojected from a current range image to a coordinate space of a previousrange image to depth values of the previous range image. Where aprojection from one coordinate space to another is executed, theprojection may be based on tracked ego-motion—e.g., recorded motion ofan ego-machine between a time associated with the previous frame and atime associated with a current frame. In addition, a current and/orprior range image may be provided directly as input to the machinelearning model. As such, the machine learning model may use the one ormore projection images, the one or more comparison images, and/or theone or more range images (or other input representations, such as atop-down view projected image) to compute a motion mask and/or a motionvector output representation.

Due to the organization and quality of information in the input channelsfor the machine learning model, the machine learning model may belightweight. For example, where the machine learning model is aconvolutional neural network (CNN), the CNN may require only very localconvolutional support—e.g., may only require ten or less layers (e.g.,six total layers, in some embodiments). In addition, the CNN may includeonly convolutional layers and no, e.g., fully connected layers or otherlayer types that may require more compute. To provide additional supportfor frames where occlusion, shading, and/or noise may weaken the inputsignals, the input channels may be computed between more than twoframes—e.g., between two or more prior frames and a current frame. Assuch, by including additional input channels corresponding to multipleprior frames, additional data may be available for the machine learningmodel in processing accurate or precise outputs that account for noise,occlusion, and/or shading.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for detecting static and dynamicfeatures from LiDAR in autonomous machine applications are described indetail below with reference to the attached drawing figures, wherein:

FIG. 1 is a data flow diagram for a static and dynamic featuresdetection system, in accordance with some embodiments of the presentdisclosure;

FIG. 2 depicts an example driving environment, in accordance with someembodiments of the present disclosure;

FIG. 3 depicts an example input image, in accordance with someembodiments of the present disclosure;

FIG. 4 is a flow diagram showing a method for computing data indicativeof motion at one or more pixels of a range image, in accordance withsome embodiments of the present disclosure;

FIG. 5A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 5B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 5A, in accordance with someembodiments of the present disclosure;

FIG. 5C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 5A, in accordance with someembodiments of the present disclosure;

FIG. 5D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 5A, in accordancewith some embodiments of the present disclosure;

FIG. 6 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 7 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to detecting static anddynamic features from LiDAR in autonomous machine applications. Althoughthe present disclosure may be described with respect to an exampleautonomous vehicle 500 (alternatively referred to herein as “vehicle500” or “ego-vehicle 500,” an example of which is described with respectto FIGS. 5A-5D), this is not intended to be limiting. For example, thesystems and methods described herein may be used by, without limitation,non-autonomous vehicles, semi-autonomous vehicles (e.g., in one or moreadaptive driver assistance systems (ADAS)), piloted and un-pilotedrobots or robotic platforms, warehouse vehicles, off-road vehicles,vehicles coupled to one or more trailers, flying vessels, boats,shuttles, emergency response vehicles, motorcycles, electric ormotorized bicycles, aircraft, construction vehicles, underwater craft,drones, and/or other vehicle types. In addition, although the presentdisclosure may be described with respect to feature and/or objecttracking in autonomous machine applications, this is not intended to belimiting, and the systems and methods described herein may be used inaugmented reality, virtual reality, mixed reality, robotics, securityand surveillance, autonomous or semi-autonomous machine applications,and/or any other technology spaces where feature and/or object trackingmay be used.

In some embodiments, as an ego-machine travels through an environment,one or more sensor(s) (e.g., LiDAR sensors, RADAR sensors, etc.) of theego-machine may generate a series of frames or sensor data, which may beconverted to one or more representations. For example, where LiDARsensors are used, raw LiDAR data may be generated at each frame or timestep, and the raw LiDAR data may be used to generate a point cloud in 3Dworld-space representing depth, elevation, and lateral locations ofpoints corresponding to features and/or objects in the environment.Using the point cloud, for example, one or more 2D image representationsmay be generated—such as a LiDAR range image, a top-down or birds eyeview image, etc.—that represent the elevation and lateral location ofpoints at one or more (x, y) pixel locations, with one or more depthvalues (and/or other values, such as intensity, reflectivity, etc.)encoded to the pixels. The depth value(s) may correspond to a distancefrom the sensor(s) to the 3D point in the environment represented by therange image.

The range images for a current frame and one or more prior frames may beused directly as input channels to a machine learning model—e.g., a deepneural network (DNN)—or may be used to generate one or more inputchannels for the machine learning model. For example, in someembodiments, depth values corresponding to a current range image may beconverted or projected to a coordinate system of a prior range image togenerate a projected image. For example, a transformation may be appliedto the 3D points corresponding to the depth value(s) in the currentrange image to locate these 3D points within the pixel index of theprevious range image(s) using a coordinate system of the previous rangeimage(s). As another example, one or more depth values corresponding toa prior range image may be converted to a coordinate space of a currentrange image to generate a projected image.

In some embodiments, an input channel may be generated by projecting acurrent range image to a coordinate system of a prior image, comparingthe projected depth value(s) to the depth value(s) of the prior image,and then generating a comparison image encoding the changes in depthvalues between frames. Where depth values are converted or projected toanother coordinate space corresponding to a different frame, theconversion or projection may be executed using ego-motioninformation—e.g., rotation, position, and/or velocity data captured byan IMU, GPS, and/or visual odometry system of the ego-machine. As such,once in a same coordinate system, where depth values differ for aparticular pixel (e.g., by more than a threshold depth difference), thismay indicate that the point or pixel corresponds to a moving or dynamicobject. In some embodiments, the depth values and location (e.g., order)of 3D points of the current range image may be preserved for use insubsequent computations where the current range image may be used as aprevious range image with a corresponding coordinate system. In additionto or alternatively from a projected or a comparison image(s), the priorrange image and/or the current range image may be provided as inputchannels directly to the machine learning model without projecting to adifferent coordinate space.

A number of input channels may depend on the number of previous framescorresponding to previous time stamps are provided to the machinelearning model. For example, the number of channels provided in an inputimage to the machine learning model may be calculated according toequation (1), below:

NUM_CHANNELS=3*NUM_FRAMES+1  (1)

where NUM_FRAMES is the number of previous frames at previous timestamps. For each prior frame, the three input channels may include acurrent frame projected to the prior frame, the prior frame projected tothe current frame, and a comparison image comparing current frame valuesprojected to the prior frame to prior frame values. Although describedas including all three channels for each frame, this is not intended tobe limiting, and in some embodiments one or more of the channels may beused for each prior frame.

With respect to a single 3D point in an environment, the channels mayrepresent, for that 3D point, a first channel associated with a distancebetween a location of the ego-machine at T₀ and the 3D point ofinterest. This first channel may correspond to directly applying therange image corresponding to a current time as an input channel, and maycorrespond to the “+1” in equation (1) above. A second channel may beassociated with a distance between a location of the ego-machine at T₀and a first 3D point at T⁻¹ that projects to the same pixel as the 3Dpoint of interest when viewed from the location of the ego-machine atT₀. A third channel may be associated with a distance between a locationof the ego-machine at T⁻¹ and the 3D point of interest. A fourth channelmay be associated with a distance between a location of the ego-machineat T⁻¹ and a second 3D point at T⁻¹ that projects to the same pixel asthe 3D point of interest when viewed from the location of theego-machine at T⁻¹.

In some embodiments, the channels may be provided as one or more inputimages (e.g., a channel stack) to the machine learning model, and themachine learning model may process the channel stack to generate amotion mask with motion confidence values (e.g., from 0 to 1) assignedto one or more (e.g., each) of the pixels indicating a confidence thatthe pixel is associated with a static or dynamic object or feature—e.g.,a pixel with a value of 0.3 may be less likely to have motion associatedtherewith than a pixel with a value of 0.9. In some embodiments, usingthe motion mask output from the machine learning model, the system mayidentify point clusters corresponding to objects and track those objectsacross frames. In further embodiments, the motion mask may be used toidentify regions of interest and may be provided as an input to aseparate object detector, which may increase the detection rate of theseparate object detector. Moreover, by tracking pixel motion betweenframes, the system may further determine vectors corresponding to 3Dpoints in addition to motion confidence values. In some embodiments, themachine learning model may be trained to compute an output of motionvectors in addition to or alternatively from confidence values. Forexample, for a given pixel in a current frame, the output may include amotion vector pointing to the same pixel (e.g., a pixel corresponding tothe same feature or object) in a prior frame.

Where the machine learning model is a DNN, such as a convolution neuralnetwork (CNN), the DNN may include a lightweight architecture, such as afully convolutional architecture consisting of only convolutionallayers. In some embodiments, there may be ten or less (e.g., six) layersin total for the DNN. The limited number of layers and the overalllightweight architecture may be possible due to the amount ofinformation and detail available in the input channels in accordancewith embodiments of the present disclosure.

As such, if the system determines that a depth value corresponding to a3D point has changed over time, the system may infer that movement hasoccurred at that particular 3D point. As a non-limiting example, if adistance from the ego-machine to a point on a wall is 10 meters in aprevious range image at time T⁻¹, and the distance from the ego-machineto the point on the wall is 5 meters in a current range image at timeT₀, then the system may determine that an object has moved into theline-of-sight of the sensor(s) of the ego-machine.

With reference to FIG. 1 , FIG. 1 is an example data flow diagram for asystem 100 for detecting static and dynamic features from sensor data inautonomous machine applications, in accordance with some embodiments ofthe present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by any (or a combination) of hardware,firmware, and/or software. For instance, various functions may becarried out by a processor executing instructions stored in memory. Insome embodiments, the system 100 may be implemented using similarcomponents, features, and/or functionality as that of example autonomousvehicle 500 of FIGS. 5A-5D, example computing device 600 of FIG. 6 ,and/or example data center 700 of FIG. 7 .

The data flow of FIG. 1 includes sensor data 110, channel generator 120,point cloud generator 122, image comparer 124, channel(s) 130, motionmodel 140, and motion mask 150. In some embodiments, the sensor data 110may include, without limitation, sensor data 110 from any of the sensorsof the vehicle 500 (and/or other vehicles, machines, or objects, such asrobotic devices, water vessels, aircraft, trains, constructionequipment, VR systems, AR systems, etc., in some examples). For anon-limiting example, such as where the sensor(s) generating the sensordata 110 are disposed on or otherwise associated with a vehicle, thesensor data 110 may include the data generated by, without limitation,global navigation satellite systems (GNSS) sensor(s) 558 (e.g., GlobalPositioning System sensor(s)), RADAR sensor(s) 560, ultrasonic sensor(s)562, LIDAR sensor(s) 564, inertial measurement unit (IMU) sensor(s) 566(e.g., accelerometer(s), gyroscope(s), magnetic compass(es),magnetometer(s), etc.), microphone(s) 596, stereo camera(s) 568,wide-view camera(s) 570 (e.g., fisheye cameras), infrared camera(s) 572,surround camera(s) 574 (e.g., 360 degree cameras), long-range and/ormid-range camera(s) 598, speed sensor(s) 544 (e.g., for measuring thespeed of the vehicle 500), and/or other sensor types.

In some embodiments, as the vehicle 500 travels through an environment,one or more sensor(s) (e.g., LiDAR sensors, RADAR sensors, etc.) of thevehicle 500 may generate the sensor data 110. The sensor data 110 may bepassed to the channel generator 120 and converted to one or morerepresentations. For example, sensor data 110—such as raw LiDAR data—maybe generated at each frame or time step as the vehicle 500 travelsthrough the environment. The point cloud generator 122 may then use thesensor data 110 from each frame or time step to generate a point cloudin 3D world-space representing depth, elevation, and lateral locationsof points corresponding to features and/or objects in the environment.Using the generated point cloud, for example, the channel generator 120may generate one or more 2D image representations—such as a LiDAR rangeimage—that may represent the elevation and lateral location of points at(x, y) pixel locations, with depth values (and/or other values, such asintensity, reflectivity, etc.) encoded to the pixels. The depth valuesof the image representation may correspond to a distance from thesensor(s) of the vehicle 500 to the 3D point in the environmentrepresented by the range image.

In some embodiments, the range images corresponding to a current frame(or time) and one or more prior frames (or times) may be output aschannel(s) 130. These channel(s) 130 may be provided to the motion model140 e.g., a deep neural network (DNN). Additionally or alternatively,the range images may be manipulated by the channel generator 120 togenerate the channel(s) 130 for the motion model 140. For example, insome embodiments, the channel generator 120 may convert or project oneor more depth values corresponding to a current range image to acoordinate system of a prior range image to generate a projected image.For example, the channel generator 120 may apply a transformation to the3D points corresponding to the one or more depth values in the currentrange image to locate these 3D points within the pixel index of theprevious range image(s) using a coordinate system of the previous rangeimage(s). In further embodiments, the channel generator 120 may convertdepth values corresponding to a prior range image to a coordinate spaceof a current range image to generate a projected image.

In some embodiments, the image comparer 124 may generate the channel(s)130 by projecting a current range image to a coordinate system of aprior image, comparing the projected depth values to the depth values ofthe prior image, and then generating a comparison image encoding thechanges in depth values between frames. Where depth values are convertedor projected to another coordinate space corresponding to a differentframe, the sensor data 110 may include ego-motion information—e.g.,rotation, position, and/or velocity data captured by an IMU, GPS, and/orvisual odometry system of the vehicle 500. Using the ego-motioninformation included in the sensor data 110, the channel generator 120may convert or project depth values to another coordinate space. Assuch, once in a same coordinate system, where depth values differ for aparticular pixel (e.g., by more than a threshold depth difference), theimage comparer 124 may determine that the point or pixel corresponds toa moving or dynamic object. In some embodiments, the depth values andlocation (e.g., order) of 3D points of the current range image may bepreserved by the channel generator 120 for use in subsequentcomputations where the current range image may be used as a previousrange image with a corresponding coordinate system. In addition to oralternatively from a projected or a comparison image(s), the prior rangeimage and/or the current range image may be provided as the channel(s)130 directly to the motion model 140 without projecting to a differentcoordinate space.

In some embodiments, a number of channel(s) 130 output by the channelgenerator 120 may depend on the number of previous frames correspondingto previous time stamps that are to be provided to the motion model 140.As a non-limiting example, for each previous frame, three input channelsmay include a current frame projected to the prior frame, the previousframe projected to the current frame, and a comparison image, from theimage comparer 124, comparing current frame values projected to theprior frame to prior frame values. Although described as including threechannels for each frame, this is not intended to be limiting, and insome embodiments one or more of the channels may be used for each priorframe.

In some embodiments, the channel(s) 130 may be provided as one or moreinput images (e.g., a channel stack) to the motion model 140. The motionmodel 140 may process the channel stack to generate the motion mask(s)150 with motion confidence values (e.g., from 0 to 1) assigned to one ormore (e.g., each) of the pixels indicating a confidence that the pixelis associated with a static or dynamic object or feature. In someembodiments, using the motion mask(s) 150 output from the motion model140, the system 100 may identify point clusters in the motion mask(s)150 corresponding to objects and track those objects across frames. Infurther embodiments, the motion mask(s) 150 may be used to identifyregions of interest and may be provided as inputs to a separate objectdetector, which may increase the detection rate of the separate objectdetector. Moreover, by tracking pixel motion between frames, the systemmay further determine vectors corresponding to 3D points in addition tomotion confidence values. In some embodiments, the motion model 140 maybe trained to compute an output of motion vectors in addition to oralternatively from confidence values. For example, for a given pixel ina current frame, the output may include a motion vector pointing to thesame pixel (e.g., a pixel corresponding to the same feature or object)in a prior frame.

Now referring to FIG. 2 , FIG. 2 is an example environment 200, inaccordance with some embodiments of the present disclosure. Theenvironment 200 includes a location 202A for the ego-machine at T⁻¹, alocation 202B for the ego-machine at T₀, ego-trajectory 204, vehiclelocation 206A, vehicle location 206B, wall 208, and 3D points 210, 212,and 214. The environment 200 illustrates an ego-machine traveling alongthe ego-trajectory 204 from the location 202A at T⁻¹ to the location202B at T₀. Additionally, FIG. 2 illustrates the effect of measuringobject distance for a pixel when a vehicle moves from the vehiclelocation 206A to the vehicle location 206B as the ego-machine moves fromthe location 202A at T⁻¹ to the location 202B at T₀.

With respect to the 3D point 210 in environment 200, generated channelsmay represent, for 3D point 210, a first channel associated with adistance between location 202B for the ego-machine at T₀ and the 3Dpoint 210. This first channel may correspond to directly applying therange image corresponding to a current time as an input channel. Asecond channel may be associated with a distance between the location202B for the ego-machine at T₀ and 3D point 212 at T⁻¹ that projects tothe same pixel as the 3D point 210 when viewed from the location 202Bfor the ego-machine at T₀. A third channel may be associated with adistance between the location 202A for the ego-machine at T⁻¹ and 3Dpoint 210. A fourth channel may be associated with a distance betweenthe location 202A for the ego-machine at T⁻¹ and the 3D point 214 at T⁻¹that projects to the same pixel as 3D point 210 when viewed from thelocation 202A for the ego-machine at T⁻¹.

As such, if the system determines that a depth value corresponding to a3D point, such as 3D point 210, has changed over time, the system mayinfer that movement has occurred at that particular 3D point. As anon-limiting example, if a first measured distance for a pixel from thelocation 202A for the ego-machine at T⁻¹ to an object (e.g., the 3Dpoint 214 on the wall 208) is 10 meters in a previous range image attime T⁻¹, and a second measured distance for the pixel from the location202B for the ego-machine at T₀ to an object (e.g., the 3D point 210) is5 meters in a current range image at time T₀, then the system maydetermine that a vehicle has moved from vehicle location 206A to vehiclelocation 206B and now obstructs the line-of-sight of the sensor(s) ofthe ego-machine at location 202B for the ego-machine at T₀. In otherwords, at T₀, the ego-machine is unable to measure the distance to the3D point 212 from the location 202B because the vehicle has moved fromvehicle location 206A to 206B. Instead, the distance measured for thepixel is the distance from the location 202B to the 3D point 210 and,based on this distance difference, the system may determine movementassociated with the pixel.

Now referring to FIG. 3 , FIG. 3 is an example input image 300, inaccordance with some embodiments of the present disclosure. The inputimage 300 includes channels 302, 304, 306, and 308. While FIG. 3 shows 4channels, the number of channels provided in the input image 300 to amachine learning model (e.g., motion model 140) may be calculatedaccording to equation (1) described above. In some examples, each priorframe may include three input channels, and the three input channels mayinclude a current frame projected to the prior frame, the prior frameprojected to the current frame, and a comparison image comparing currentframe values projected to the prior frame to prior frame values.Although described as including all three channels for each prior frame,this is not intended to be limiting, and in some embodiments one or moreof the channels may be used for each prior frame.

Using, as an example, the environment 200, the locations 202A/B, and the3D points 210, 212, and 214 of FIG. 2 , each of the channels 302, 304,306, and 308 may include distance information. For example, channel 302may include a distance between the location 202B for the ego-machine atT₀ and the 3D point 210, channel 304 may include a distance between thelocation 202B for the ego-machine at T₀ and 3D point 212, channel 306may include a distance between the location 202A for the ego-machine atT⁻¹ and 3D point 210, and channel 308 may include a distance between thelocation 202A for the ego-machine at T⁻¹ and the 3D point 214. The inputimage 300 with each of these channels and corresponding distanceinformation may be provided to the machine learning model to determinestatic and dynamic objects in an environment, such as in environment 200of FIG. 2 .

Where the machine learning model is a DNN, such as a convolution neuralnetwork (CNN), the DNN may include a lightweight architecture, such as afully convolutional architecture consisting of only convolutionallayers. In some embodiments, there may be ten or less (e.g., six) layersin total for the DNN. The limited number of layers and the overalllightweight architecture may be possible due to the amount ofinformation and detail available in the input image 300.

Although examples are described herein with respect to using DNNs, andspecifically convolutional neural networks (CNNs), this is not intendedto be limiting. For example, and without limitation, the DNN(s) 126 mayinclude any type of machine learning model, such as a machine learningmodel(s) using linear regression, logistic regression, decision trees,support vector machines (SVM), Naïve Bayes, k-nearest neighbor (Knn), Kmeans clustering, random forest, dimensionality reduction algorithms,gradient boosting algorithms, neural networks (e.g., auto-encoders,convolutional, recurrent, perceptrons, long/short term memory/LSTM,Hopfield, Boltzmann, deep belief, deconvolutional, generativeadversarial, liquid state machine, etc.), areas of interest detectionalgorithms, computer vision algorithms, and/or other types of machinelearning models.

In some embodiments, where a DNN is used, the DNN may include any numberof layers. One or more layers may include convolutional layers. Theconvolutional layers may compute the output of neurons that areconnected to local regions in an input layer, each neuron computing adot product between their weights and a small region they are connectedto in the input volume. One or more of the layers may include arectified linear unit (ReLU) layer. The ReLU layer(s) may apply anelementwise activation function, such as the max (0, x), thresholding atzero, for example. The resulting volume of a ReLU layer may be the sameas the volume of the input of the ReLU layer. One or more of the layersmay include a pooling layer. The pooling layer may perform a downsampling operation along the spatial dimensions (e.g., the height andthe width), which may result in a smaller volume than the input of thepooling layer. One or more of the layers may include one or more fullyconnected layer(s). Each neuron in the fully connected layer(s) may beconnected to each of the neurons in the previous volume. The fullyconnected layer may compute class scores, and the resulting volume maybe 1×1×number of classes. In some examples, the CNN may include a fullyconnected layer(s) such that the output of one or more of the layers ofthe CNN may be provided as input to a fully connected layer(s) of theCNN. In some examples, one or more convolutional streams may beimplemented by the DNN, and some or all of the convolutional streams mayinclude a respective fully connected layer(s). In some non-limitingembodiments, the DNN may include a series of convolutional and maxpooling layers to facilitate image feature extraction, followed bymulti-scale dilated convolutional and up-sampling layers to facilitateglobal context feature extraction.

Although input layers, convolutional layers, pooling layers, ReLUlayers, and fully connected layers are discussed herein with respect tothe DNN, this is not intended to be limiting. For example, additional oralternative layers may be used in the DNN, such as normalization layers,SoftMax layers, and/or other layer types. In embodiments where the DNNincludes a CNN, different orders and numbers of the layers of the CNNmay be used depending on the embodiment. In other words, the order andnumber of layers of the DNN is not limited to any one architecture.

In addition, some of the layers may include parameters (e.g., weightsand/or biases), such as the convolutional layers and the fully connectedlayers, while others may not, such as the ReLU layers and poolinglayers. In some examples, the parameters may be learned by the DNNduring training. Further, some of the layers may include additionalhyper-parameters (e.g., learning rate, stride, epochs, etc.), such asthe convolutional layers, the fully connected layers, and the poolinglayers, while other layers may not, such as the ReLU layers. Theparameters and hyper-parameters are not to be limited and may differdepending on the embodiment.

Now referring to FIG. 4 , each block of method 400, described herein,comprises a computing process that may be performed using anycombination of hardware, firmware, and/or software. For instance,various functions may be carried out by a processor executinginstructions stored in memory. The methods may also be embodied ascomputer-usable instructions stored on computer storage media. Themethods may be provided by a standalone application, a service or hostedservice (standalone or in combination with another hosted service), or aplug-in to another product, to name a few. In addition, method 400 isdescribed, by way of example, with respect to the system for detectingstatic and dynamic features of FIG. 1 . However, these methods mayadditionally or alternatively be executed by any one system, or anycombination of systems, including, but not limited to, those describedherein.

FIG. 4 is a flow diagram showing a method 400 for computing dataindicative of motion at one or more pixels of a range image, inaccordance with some embodiments of the present disclosure. The method400, at block B402, includes generating, using a LiDAR sensor of anego-machine, a first range image at a first time and a second rangeimage at a second time subsequent the first time. For example, whereLiDAR sensors are used, raw LiDAR data may be generated at each frame ortime step, and the raw LiDAR data may be used to generate a point cloudin 3D world-space representing depth, elevation, and lateral locationsof points corresponding to features and/or objects in the environment.Using the point cloud, for example, one or more 2D image representationsmay be generated—such as a LiDAR range image, a top-down or birds eyeview image, etc.—that represent the elevation and lateral location ofpoints at (x, y) pixel locations, with depth values (and/or othervalues, such as intensity, reflectivity, etc.) encoded to the pixels.

The method 400, at block B404, includes generating a first projectedimage based at least in part on projecting first depth values from thefirst range image to a first coordinate space of the second range imageand a second projected image based at least in part on projecting seconddepth values from the second range image to a second coordinate space ofthe first range image. For example, one or more depth valuescorresponding to a current range image may be converted or projected toa coordinate system of a prior range image to generate a projectedimage. For example, a transformation may be applied to the 3D pointscorresponding to the depth values in the current range image to locatethese 3D points within the pixel index of the previous range image(s)using a coordinate system of the previous range image(s).

The method 400, at block B406, includes computing, using a deep neuralnetwork (DNN) and based at least in part on the second range image, thefirst projected image, and the second projected image, data indicativeof motion at one or more pixels of the second range image. For example,channels may be provided as one or more input images (e.g., a channelstack) to the motion model 140 (e.g., a machine learning model), and themotion model 140 may process the channel stack to generate a motion maskwith motion confidence values (e.g., from 0 to 1) assigned to one ormore (e.g., each) of the pixels indicating a confidence that the pixelis associated with a static or dynamic object or feature—e.g., a pixelwith a value of 0.3 may be less likely to have motion associatedtherewith than a pixel with a value of 0.9.

Example Autonomous Vehicle

FIG. 5A is an illustration of an example autonomous vehicle 500, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 500 (alternatively referred to herein as the “vehicle500”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,a vehicle coupled to a trailer, and/or another type of vehicle (e.g.,that is unmanned and/or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 500 may be capable offunctionality in accordance with one or more of Level 3—Level 5 of theautonomous driving levels. For example, the vehicle 500 may be capableof conditional automation (Level 3), high automation (Level 4), and/orfull automation (Level 5), depending on the embodiment.

The vehicle 500 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 500 may include a propulsion system550, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 550 may be connected to a drive train of the vehicle500, which may include a transmission, to enable the propulsion of thevehicle 500. The propulsion system 550 may be controlled in response toreceiving signals from the throttle/accelerator 552.

A steering system 554, which may include a steering wheel, may be usedto steer the vehicle 500 (e.g., along a desired path or route) when thepropulsion system 550 is operating (e.g., when the vehicle is inmotion). The steering system 554 may receive signals from a steeringactuator 556. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 546 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 548 and/or brakesensors.

Controller(s) 536, which may include one or more system on chips (SoCs)504 (FIG. 5C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle500. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 548, to operate thesteering system 554 via one or more steering actuators 556, to operatethe propulsion system 550 via one or more throttle/accelerators 552. Thecontroller(s) 536 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 500. The controller(s) 536 may include a first controller 536for autonomous driving functions, a second controller 536 for functionalsafety functions, a third controller 536 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 536 forinfotainment functionality, a fifth controller 536 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 536 may handle two or more of the abovefunctionalities, two or more controllers 536 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 536 may provide the signals for controlling one ormore components and/or systems of the vehicle 500 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 558 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 560, ultrasonic sensor(s) 562, LIDARsensor(s) 564, inertial measurement unit (IMU) sensor(s) 566 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 596, stereo camera(s) 568, wide-view camera(s) 570(e.g., fisheye cameras), infrared camera(s) 572, surround camera(s) 574(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 598,speed sensor(s) 544 (e.g., for measuring the speed of the vehicle 500),vibration sensor(s) 542, steering sensor(s) 540, brake sensor(s) (e.g.,as part of the brake sensor system 546), and/or other sensor types.

One or more of the controller(s) 536 may receive inputs (e.g.,represented by input data) from an instrument cluster 532 of the vehicle500 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 534, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle500. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 522 of FIG. 5C), location data(e.g., the vehicle's 500 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 536,etc. For example, the HMI display 534 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.).

The vehicle 500 further includes a network interface 524 which may useone or more wireless antenna(s) 526 and/or modem(s) to communicate overone or more networks. For example, the network interface 524 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 526 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc.

FIG. 5B is an example of camera locations and fields of view for theexample autonomous vehicle 500 of FIG. 5A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle500.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 500. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 500 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 536 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 570 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.5B, there may any number of wide-view cameras 570 on the vehicle 500. Inaddition, long-range camera(s) 598 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 598 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 568 may also be included in a front-facingconfiguration. The stereo camera(s) 568 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 568 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 568 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 500 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 574 (e.g., four surround cameras 574 asillustrated in FIG. 5B) may be positioned to on the vehicle 500. Thesurround camera(s) 574 may include wide-view camera(s) 570, fisheyecamera(s), 360 degree camera(s), and/or the like. Four example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 574 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 500 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 598,stereo camera(s) 568), infrared camera(s) 572, etc.), as describedherein.

FIG. 5C is a block diagram of an example system architecture for theexample autonomous vehicle 500 of FIG. 5A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 500 in FIG.5C are illustrated as being connected via bus 502. The bus 502 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 500 used to aid in control of various features and functionalityof the vehicle 500, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 502 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 502, this is notintended to be limiting. For example, there may be any number of busses502, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses502 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 502 may be used for collisionavoidance functionality and a second bus 502 may be used for actuationcontrol. In any example, each bus 502 may communicate with any of thecomponents of the vehicle 500, and two or more busses 502 maycommunicate with the same components. In some examples, each SoC 504,each controller 536, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle500), and may be connected to a common bus, such the CAN bus.

The vehicle 500 may include one or more controller(s) 536, such as thosedescribed herein with respect to FIG. 5A. The controller(s) 536 may beused for a variety of functions. The controller(s) 536 may be coupled toany of the various other components and systems of the vehicle 500, andmay be used for control of the vehicle 500, artificial intelligence ofthe vehicle 500, infotainment for the vehicle 500, and/or the like.

The vehicle 500 may include a system(s) on a chip (SoC) 504. The SoC 504may include CPU(s) 506, GPU(s) 508, processor(s) 510, cache(s) 512,accelerator(s) 514, data store(s) 516, and/or other components andfeatures not illustrated. The SoC(s) 504 may be used to control thevehicle 500 in a variety of platforms and systems. For example, theSoC(s) 504 may be combined in a system (e.g., the system of the vehicle500) with an HD map 522 which may obtain map refreshes and/or updatesvia a network interface 524 from one or more servers (e.g., server(s)578 of FIG. 5D).

The CPU(s) 506 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 506 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 506may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 506 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 506 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)506 to be active at any given time.

The CPU(s) 506 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 506may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 508 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 508 may be programmable and may beefficient for parallel workloads. The GPU(s) 508, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 508 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 508 may include at least eight streamingmicroprocessors. The GPU(s) 508 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 508 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 508 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 508 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 508 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 508 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 508 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 508 to access the CPU(s) 506 page tables directly. Insuch examples, when the GPU(s) 508 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 506. In response, the CPU(s) 506 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 508. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 506 and the GPU(s) 508, thereby simplifying the GPU(s) 508programming and porting of applications to the GPU(s) 508.

In addition, the GPU(s) 508 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 508 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 504 may include any number of cache(s) 512, including thosedescribed herein. For example, the cache(s) 512 may include an L3 cachethat is available to both the CPU(s) 506 and the GPU(s) 508 (e.g., thatis connected both the CPU(s) 506 and the GPU(s) 508). The cache(s) 512may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 504 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 500—such as processingDNNs. In addition, the SoC(s) 504 may include a floating point unit(s)(FPU(s))—or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 104 may include one or more FPUs integrated as execution unitswithin a CPU(s) 506 and/or GPU(s) 508.

The SoC(s) 504 may include one or more accelerators 514 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 504 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 508 and to off-load some of the tasks of theGPU(s) 508 (e.g., to free up more cycles of the GPU(s) 508 forperforming other tasks). As an example, the accelerator(s) 514 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 508, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 508 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 508 and/or other accelerator(s) 514.

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 506. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMM), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 514 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 514. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 504 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 514 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 566 output thatcorrelates with the vehicle 500 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 564 or RADAR sensor(s) 560), amongothers.

The SoC(s) 504 may include data store(s) 516 (e.g., memory). The datastore(s) 516 may be on-chip memory of the SoC(s) 504, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 516 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 512 may comprise L2 or L3 cache(s) 512. Reference to thedata store(s) 516 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 514, as described herein.

The SoC(s) 504 may include one or more processor(s) 510 (e.g., embeddedprocessors). The processor(s) 510 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 504 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 504 thermals and temperature sensors, and/ormanagement of the SoC(s) 504 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 504 may use thering-oscillators to detect temperatures of the CPU(s) 506, GPU(s) 508,and/or accelerator(s) 514. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 504 into a lower powerstate and/or put the vehicle 500 into a chauffeur to safe stop mode(e.g., bring the vehicle 500 to a safe stop).

The processor(s) 510 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 510 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 510 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 510 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 510 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 510 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)570, surround camera(s) 574, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 508 is not required tocontinuously render new surfaces. Even when the GPU(s) 508 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 508 to improve performance and responsiveness.

The SoC(s) 504 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 504 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 504 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 504 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 564, RADAR sensor(s) 560,etc. that may be connected over Ethernet), data from bus 502 (e.g.,speed of vehicle 500, steering wheel position, etc.), data from GNSSsensor(s) 558 (e.g., connected over Ethernet or CAN bus). The SoC(s) 504may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 506 from routine data management tasks.

The SoC(s) 504 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 504 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 514, when combined with the CPU(s) 506, the GPU(s) 508,and the data store(s) 516, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 520) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 508.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 500. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 504 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 596 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 504 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)558. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 562, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 518 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 504 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 518 may include an X86 processor,for example. The CPU(s) 518 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 504, and/or monitoring the statusand health of the controller(s) 536 and/or infotainment SoC 530, forexample.

The vehicle 500 may include a GPU(s) 520 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 504 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 520 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 500.

The vehicle 500 may further include the network interface 524 which mayinclude one or more wireless antennas 526 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 524 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 578 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 500information about vehicles in proximity to the vehicle 500 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 500).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 500.

The network interface 524 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 536 tocommunicate over wireless networks. The network interface 524 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 500 may further include data store(s) 528 which may includeoff-chip (e.g., off the SoC(s) 504) storage. The data store(s) 528 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 500 may further include GNSS sensor(s) 558. The GNSSsensor(s) 558 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)558 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 500 may further include RADAR sensor(s) 560. The RADARsensor(s) 560 may be used by the vehicle 500 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 560 may usethe CAN and/or the bus 502 (e.g., to transmit data generated by theRADAR sensor(s) 560) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 560 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 560 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 560may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 500 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 500 lane.

Mid-range RADAR systems may include, as an example, a range of up to 560m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 550 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 500 may further include ultrasonic sensor(s) 562. Theultrasonic sensor(s) 562, which may be positioned at the front, back,and/or the sides of the vehicle 500, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 562 may be used, and different ultrasonic sensor(s) 562 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 562 may operate at functional safety levels of ASILB.

The vehicle 500 may include LIDAR sensor(s) 564. The LIDAR sensor(s) 564may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 564 maybe functional safety level ASIL B. In some examples, the vehicle 500 mayinclude multiple LIDAR sensors 564 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 564 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 564 may have an advertised rangeof approximately 500 m, with an accuracy of 2 cm-3 cm, and with supportfor a 500 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 564 may be used. In such examples,the LIDAR sensor(s) 564 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 500.The LIDAR sensor(s) 564, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)564 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 500. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)564 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 566. The IMU sensor(s) 566may be located at a center of the rear axle of the vehicle 500, in someexamples. The IMU sensor(s) 566 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 566 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 566 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 566 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 566 may enable the vehicle 500to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 566. In some examples, the IMU sensor(s) 566 and theGNSS sensor(s) 558 may be combined in a single integrated unit.

The vehicle may include microphone(s) 596 placed in and/or around thevehicle 500. The microphone(s) 596 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 568, wide-view camera(s) 570, infrared camera(s) 572,surround camera(s) 574, long-range and/or mid-range camera(s) 598,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 500. The types of cameras useddepends on the embodiments and requirements for the vehicle 500, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 500. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 5A and FIG. 5B.

The vehicle 500 may further include vibration sensor(s) 542. Thevibration sensor(s) 542 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 542 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 500 may include an ADAS system 538. The ADAS system 538 mayinclude a SoC, in some examples. The ADAS system 538 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 560, LIDAR sensor(s) 564, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 500 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 500 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 524 and/or the wireless antenna(s) 526 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 500), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 500, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 560, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 560, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle500 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 500 if the vehicle 500 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)560, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 500 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 560, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 500, the vehicle 500itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 536 or a second controller 536). For example, in someembodiments, the ADAS system 538 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 538may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 504.

In other examples, ADAS system 538 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 538 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 538indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 500 may further include the infotainment SoC 530 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 530 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 500. For example, the infotainment SoC 530 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 534, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 530 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 538,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 530 may include GPU functionality. The infotainmentSoC 530 may communicate over the bus 502 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 500. Insome examples, the infotainment SoC 530 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 536(e.g., the primary and/or backup computers of the vehicle 500) fail. Insuch an example, the infotainment SoC 530 may put the vehicle 500 into achauffeur to safe stop mode, as described herein.

The vehicle 500 may further include an instrument cluster 532 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 532 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 532 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 530 and theinstrument cluster 532. In other words, the instrument cluster 532 maybe included as part of the infotainment SoC 530, or vice versa.

FIG. 5D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 500 of FIG. 5A, inaccordance with some embodiments of the present disclosure. The system576 may include server(s) 578, network(s) 590, and vehicles, includingthe vehicle 500. The server(s) 578 may include a plurality of GPUs584(A)-584(H) (collectively referred to herein as GPUs 584), PCIeswitches 582(A)-582(H) (collectively referred to herein as PCIe switches582), and/or CPUs 580(A)-580(B) (collectively referred to herein as CPUs580). The GPUs 584, the CPUs 580, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 588 developed by NVIDIA and/orPCIe connections 586. In some examples, the GPUs 584 are connected viaNVLink and/or NVSwitch SoC and the GPUs 584 and the PCIe switches 582are connected via PCIe interconnects. Although eight GPUs 584, two CPUs580, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 578 mayinclude any number of GPUs 584, CPUs 580, and/or PCIe switches. Forexample, the server(s) 578 may each include eight, sixteen, thirty-two,and/or more GPUs 584.

The server(s) 578 may receive, over the network(s) 590 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 578 may transmit, over the network(s) 590 and to the vehicles,neural networks 592, updated neural networks 592, and/or map information594, including information regarding traffic and road conditions. Theupdates to the map information 594 may include updates for the HD map522, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 592, the updated neural networks 592, and/or the mapinformation 594 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 578 and/or other servers).

The server(s) 578 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 590, and/or the machine learningmodels may be used by the server(s) 578 to remotely monitor thevehicles.

In some examples, the server(s) 578 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 578 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 584, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 578 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 578 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 500. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 500, suchas a sequence of images and/or objects that the vehicle 500 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 500 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 500 is malfunctioning, the server(s) 578 may transmit asignal to the vehicle 500 instructing a fail-safe computer of thevehicle 500 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 578 may include the GPU(s) 584 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 6 is a block diagram of an example computing device(s) 600 suitablefor use in implementing some embodiments of the present disclosure.Computing device 600 may include an interconnect system 602 thatdirectly or indirectly couples the following devices: memory 604, one ormore central processing units (CPUs) 606, one or more graphicsprocessing units (GPUs) 608, a communication interface 610, input/output(I/O) ports 612, input/output components 614, a power supply 616, one ormore presentation components 618 (e.g., display(s)), and one or morelogic units 620. In at least one embodiment, the computing device(s) 600may comprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs608 may comprise one or more vGPUs, one or more of the CPUs 606 maycomprise one or more vCPUs, and/or one or more of the logic units 620may comprise one or more virtual logic units. As such, a computingdevice(s) 600 may include discrete components (e.g., a full GPUdedicated to the computing device 600), virtual components (e.g., aportion of a GPU dedicated to the computing device 600), or acombination thereof.

Although the various blocks of FIG. 6 are shown as connected via theinterconnect system 602 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 618, such as a display device, may be consideredan I/O component 614 (e.g., if the display is a touch screen). Asanother example, the CPUs 606 and/or GPUs 608 may include memory (e.g.,the memory 604 may be representative of a storage device in addition tothe memory of the GPUs 608, the CPUs 606, and/or other components). Inother words, the computing device of FIG. 6 is merely illustrative.Distinction is not made between such categories as “workstation,”“server,” “laptop,” “desktop,” “tablet,” “client device,” “mobiledevice,” “hand-held device,” “game console,” “electronic control unit(ECU),” “virtual reality system,” and/or other device or system types,as all are contemplated within the scope of the computing device of FIG.6 .

The interconnect system 602 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 602 may include one or more bus or linktypes, such as an industry standard architecture (ISA) bus, an extendedindustry standard architecture (EISA) bus, a video electronics standardsassociation (VESA) bus, a peripheral component interconnect (PCI) bus, aperipheral component interconnect express (PCIe) bus, and/or anothertype of bus or link. In some embodiments, there are direct connectionsbetween components. As an example, the CPU 606 may be directly connectedto the memory 604. Further, the CPU 606 may be directly connected to theGPU 608. Where there is direct, or point-to-point connection betweencomponents, the interconnect system 602 may include a PCIe link to carryout the connection. In these examples, a PCI bus need not be included inthe computing device 600.

The memory 604 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 600. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 604 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device600. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 606 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 600 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 606 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 606 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 600 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 600, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 600 mayinclude one or more CPUs 606 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 606, the GPU(s) 608 maybe configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device600 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 608 may be an integrated GPU (e.g.,with one or more of the CPU(s) 606 and/or one or more of the GPU(s) 608may be a discrete GPU. In embodiments, one or more of the GPU(s) 608 maybe a coprocessor of one or more of the CPU(s) 606. The GPU(s) 608 may beused by the computing device 600 to render graphics (e.g., 3D graphics)or perform general purpose computations. For example, the GPU(s) 608 maybe used for General-Purpose computing on GPUs (GPGPU). The GPU(s) 608may include hundreds or thousands of cores that are capable of handlinghundreds or thousands of software threads simultaneously. The GPU(s) 608may generate pixel data for output images in response to renderingcommands (e.g., rendering commands from the CPU(s) 606 received via ahost interface). The GPU(s) 608 may include graphics memory, such asdisplay memory, for storing pixel data or any other suitable data, suchas GPGPU data. The display memory may be included as part of the memory604. The GPU(s) 608 may include two or more GPUs operating in parallel(e.g., via a link). The link may directly connect the GPUs (e.g., usingNVLINK) or may connect the GPUs through a switch (e.g., using NVSwitch).When combined together, each GPU 608 may generate pixel data or GPGPUdata for different portions of an output or for different outputs (e.g.,a first GPU for a first image and a second GPU for a second image). EachGPU may include its own memory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 606 and/or the GPU(s)608, the logic unit(s) 620 may be configured to execute at least some ofthe computer-readable instructions to control one or more components ofthe computing device 600 to perform one or more of the methods and/orprocesses described herein. In embodiments, the CPU(s) 606, the GPU(s)608, and/or the logic unit(s) 620 may discretely or jointly perform anycombination of the methods, processes and/or portions thereof. One ormore of the logic units 620 may be part of and/or integrated in one ormore of the CPU(s) 606 and/or the GPU(s) 608 and/or one or more of thelogic units 620 may be discrete components or otherwise external to theCPU(s) 606 and/or the GPU(s) 608. In embodiments, one or more of thelogic units 620 may be a coprocessor of one or more of the CPU(s) 606and/or one or more of the GPU(s) 608.

Examples of the logic unit(s) 620 include one or more processing coresand/or components thereof, such as Data Processing Units (DPUs), TensorCores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs),Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs),Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs),Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 610 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 600to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 610 may include components and functionalityto enable communication over any of a number of different networks, suchas wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE,ZigBee, etc.), wired networks (e.g., communicating over Ethernet orInfiniBand), low-power wide-area networks (e.g., LoRaWAN, SigFox, etc.),and/or the Internet. In one or more embodiments, logic unit(s) 620and/or communication interface 610 may include one or more dataprocessing units (DPUs) to transmit data received over a network and/orthrough interconnect system 602 directly to (e.g., a memory of) one ormore GPU(s) 608.

The I/O ports 612 may enable the computing device 600 to be logicallycoupled to other devices including the I/O components 614, thepresentation component(s) 618, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 600.Illustrative I/O components 614 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 614 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 600. Thecomputing device 600 may be include depth cameras, such as stereoscopiccamera systems, infrared camera systems, RGB camera systems, touchscreentechnology, and combinations of these, for gesture detection andrecognition. Additionally, the computing device 600 may includeaccelerometers or gyroscopes (e.g., as part of an inertia measurementunit (IMU)) that enable detection of motion. In some examples, theoutput of the accelerometers or gyroscopes may be used by the computingdevice 600 to render immersive augmented reality or virtual reality.

The power supply 616 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 616 may providepower to the computing device 600 to enable the components of thecomputing device 600 to operate.

The presentation component(s) 618 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 618 may receivedata from other components (e.g., the GPU(s) 608, the CPU(s) 606, DPUs,etc.), and output the data (e.g., as an image, video, sound, etc.).

Example Data Center

FIG. 7 illustrates an example data center 700 that may be used in atleast one embodiments of the present disclosure. The data center 700 mayinclude a data center infrastructure layer 710, a framework layer 720, asoftware layer 730, and/or an application layer 740.

As shown in FIG. 7 , the data center infrastructure layer 710 mayinclude a resource orchestrator 712, grouped computing resources 714,and node computing resources (“node C.R.s”) 716(1)-716(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 716(1)-716(N) may include, but are not limited to, any number ofcentral processing units (CPUs) or other processors (including DPUs,accelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (NW I/O) devices, network switches,virtual machines (VMs), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s716(1)-716(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 716(1)-7161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 716(1)-716(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 714 may includeseparate groupings of node C.R.s 716 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 716 within grouped computing resources 714 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 716 including CPUs, GPUs, DPUs, and/or otherprocessors may be grouped within one or more racks to provide computeresources to support one or more workloads. The one or more racks mayalso include any number of power modules, cooling modules, and/ornetwork switches, in any combination.

The resource orchestrator 712 may configure or otherwise control one ormore node C.R.s 716(1)-716(N) and/or grouped computing resources 714. Inat least one embodiment, resource orchestrator 712 may include asoftware design infrastructure (SDI) management entity for the datacenter 700. The resource orchestrator 712 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 7 , framework layer 720 mayinclude a job scheduler 732, a configuration manager 734, a resourcemanager 736, and/or a distributed file system 738. The framework layer720 may include a framework to support software 732 of software layer730 and/or one or more application(s) 742 of application layer 740. Thesoftware 732 or application(s) 742 may respectively include web-basedservice software or applications, such as those provided by Amazon WebServices, Google Cloud and Microsoft Azure. The framework layer 720 maybe, but is not limited to, a type of free and open-source software webapplication framework such as Apache Spark™ (hereinafter “Spark”) thatmay utilize distributed file system 738 for large-scale data processing(e.g., “big data”). In at least one embodiment, job scheduler 732 mayinclude a Spark driver to facilitate scheduling of workloads supportedby various layers of data center 700. The configuration manager 734 maybe capable of configuring different layers such as software layer 730and framework layer 720 including Spark and distributed file system 738for supporting large-scale data processing. The resource manager 736 maybe capable of managing clustered or grouped computing resources mappedto or allocated for support of distributed file system 738 and jobscheduler 732. In at least one embodiment, clustered or groupedcomputing resources may include grouped computing resource 714 at datacenter infrastructure layer 710. The resource manager 736 may coordinatewith resource orchestrator 712 to manage these mapped or allocatedcomputing resources.

In at least one embodiment, software 732 included in software layer 730may include software used by at least portions of node C.R.s716(1)-716(N), grouped computing resources 714, and/or distributed filesystem 738 of framework layer 720. One or more types of software mayinclude, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 742 included in applicationlayer 740 may include one or more types of applications used by at leastportions of node C.R.s 716(1)-716(N), grouped computing resources 714,and/or distributed file system 738 of framework layer 720. One or moretypes of applications may include, but are not limited to, any number ofa genomics application, a cognitive compute, and a machine learningapplication, including training or inferencing software, machinelearning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.),and/or other machine learning applications used in conjunction with oneor more embodiments.

In at least one embodiment, any of configuration manager 734, resourcemanager 736, and resource orchestrator 712 may implement any number andtype of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 700 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 700 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center 700.In at least one embodiment, trained or deployed machine learning modelscorresponding to one or more neural networks may be used to infer orpredict information using resources described above with respect to thedata center 700 by using weight parameters calculated through one ormore training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 700 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 600 of FIG. 6 —e.g., each device may include similarcomponents, features, and/or functionality of the computing device(s)600. In addition, where backend devices (e.g., servers, NAS, etc.) areimplemented, the backend devices may be included as part of a datacenter 700, an example of which is described in more detail herein withrespect to FIG. 7 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 600described herein with respect to FIG. 6 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A processor comprising: one or more circuits to:generate, using LiDAR data captured using a LiDAR sensor of anego-machine, a first range image corresponding to a first time and asecond range image corresponding to a second time subsequent the firsttime; generate a first projected image based at least in part onprojecting first depth values from the first range image to a firstcoordinate space of the second range image and a second projected imagebased at least in part on projecting second depth values from the secondrange image to a second coordinate space of the first range image; andcompute, using a deep neural network (DNN) and based at least in part onthe second range image, the first projected image, and the secondprojected image, data indicative of motion at one or more pixels of thesecond range image.
 2. The processor of claim 1, further comprising oneor more circuits to: generate a comparison image based at least in parton part on comparing the second depth values from the second range imagein the second coordinate space of the first range image to depth valuesof the first range image; and wherein computing the data indicative ofmotion is further based at least in part on the comparison image.
 3. Theprocessor of claim 2, wherein, for each range image of a set of n numberof range images corresponding to a respective set of n times prior tothe second time, one or more of: a respective first projected image, asecond projected image, or a comparison image are generated using thesecond range image.
 4. The processor of claim 1, wherein the firstprojected image and the second projected image are generated usingtracked ego-motion of the ego-machine between the first time and thesubsequent time.
 5. The processor of claim 1, wherein the DNN includesat least one of: a convolutional neural network including one or moreconvolutional layers or a number of total layers less than ten totallayers.
 6. The processor of claim 1, wherein the data computed using theDNN is representative of a motion mask, the motion mask being indicativeof one or more objects depicted using the second range image being inmotion at the second time.
 7. The processor of claim 6, wherein themotion mask comprises, for each of the one or more pixels of the secondrange image, a confidence value between 0 and 1, the confidence valuebeing indicative of whether a feature or object represented using arespective pixel of the one or more pixels corresponds to an object inmotion at the second time.
 8. The processor of claim 1, wherein the datacomputed using the DNN is further indicative of one or motion vectorsindicative of movement of pixels between the first range image and thesecond range image.
 9. The processor of claim 1, further comprising oneor more circuits to compute, based at least in part on the data computedusing the DNN, one or more object detections.
 10. The processor of claim1, wherein the processor is comprised in at least one of: a controlsystem for an autonomous or semi-autonomous machine; a perception systemfor an autonomous or semi-autonomous machine; a system for performingsimulation operations; a system for performing deep learning operations;a system implemented using an edge device; a system implemented using arobot; a system incorporating one or more virtual machines (VMs); asystem implemented at least partially in a data center; or a systemimplemented at least partially using cloud computing resources.
 11. Asystem comprising: one or more processing units; and one or more memoryunits storing instructions that, when executed by the one or moreprocessing units, cause the one or more processing units to executeoperations comprising: generating a first projected image based at leastin part on projecting one or more first depth values from a first rangeimage generated at a first time to a first coordinate space of a secondrange image generated at a second time subsequent the first time;generating a second projected image based at least in part on projectingone or more second depth values from the second range image to a secondcoordinate space of the first range image; generating a comparison imagebased at least in part on part on comparing the second depth values fromthe second range image in the second coordinate space of the first rangeimage to depth values of the first range image; and computing, using adeep neural network (DNN) and based at least in part on the second rangeimage, the first projected image, the second projected image, and thecomparison image, data indicative of an object in motion at one or morepixels of the second range image.
 12. The system of claim 11, whereinthe generating the comparison image includes encoding values of thedifference between the second depth values in the second coordinatespace and the depth values to one or more pixels of the comparisonimage.
 13. The system of claim 11, wherein the operations furthercomprise: applying the data computed using the DNN to an objectdetector; and computing, using the object detector and based at least inpart on the data, one or more object detections.
 14. The system of claim11, wherein the generation of the first projected image, the secondprojected image, and the comparison image are based at least in part ontracked ego-motion of the ego-machine between the first time and thesecond time.
 15. The system of claim 11, wherein the DNN includes aconvolutional neural network including only convolutional layers. 16.The system of claim 11, wherein the system is comprised in at least oneof: a control system for an autonomous or semi-autonomous machine; aperception system for an autonomous or semi-autonomous machine; a systemfor performing simulation operations; a system for performing deeplearning operations; a system implemented using an edge device; a systemimplemented using a robot; a system incorporating one or more virtualmachines (VMs); a system implemented at least partially in a datacenter; or a system implemented at least partially using cloud computingresources.
 17. A method comprising: generating, using a LiDAR sensor ofan ego-machine, a first range image at a first time and a second rangeimage at a second time subsequent the first time; generating a firstprojected image based at least in part on projecting one or more firstdepth values from the first range image to a first coordinate space ofthe second range image; generating a second projected image based atleast in part on projecting one or more second depth values from thesecond range image to a second coordinate space of the first rangeimage; and computing, using a deep neural network (DNN) and based atleast in part on the second range image, the first projected image, andthe second projected image, data indicative of motion at one or morepixels of the second range image.
 18. The method of claim 17, furthercomprising: generating a comparison image based at least in part on parton comparing the second depth values from the second range image in thesecond coordinate space of the first range image to depth values of thefirst range image, wherein the computing the data is further based atleast in part on the comparison image.
 19. The method of claim 18,wherein, for each range image of n number of range images prior to thesecond range image, a respective first projected image, second projectedimage, and comparison image are generated using the second range image.20. The method of claim 17, wherein the generating the first projectedimage and the second projected image are based at least in part ontracked ego-motion of the ego-machine between the first time and thesubsequent time.